Popular Data Mining Tools (Types, Examples And Uses)

By Indeed Editorial Team

Updated 29 October 2022

Published 15 November 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data mining tools refine and organise scattered data in the form of charts, graphs and tables that are easy to understand. These tools help researchers and scientists to analyse and understand the vast amount of data to reach relevant conclusions and findings. If you are thinking to pursue a career in the field of data analysis or research, understanding these various tools can help you advance your career in the industry. In this article, we discuss what data mining tools are and explore their different types with examples.

Related: 12 Common Data Mining Interview Questions (With Answers)

What Are Data Mining Tools?

Data mining tools aim to find patterns, trends and groupings among large sets of data. They transform data into more organised and relevant information. These tools are frameworks, such as Hadoop MapReduce or Tableau, allowing users to perform different types of data mining analysis. They also enable users to analyse, simulate, plan and predict data on a single platform.

Related: 13 Data Mining Techniques: A Complete Guide

Types Of Mining Tools

There are many mining tools, and classifying them according to their primary use-case can make it easier to understand them:

  • Integrated mining tools for statistical analysis

  • Open-source data mining solutions

  • Data mining tools for big data

  • Small scale solutions for data mining

  • Cloud solutions for data mining

  • Mining tools for neural networks

  • Mining tools for data visualisation

Integrated mining tools for statistical analysis

Following are some of the integrated mining tools for statistical analysis:

R

R first appeared in August 1993 and currently, the R core team of R foundation manages this software. It is a free programming language and a software environment used for statistical modelling, data analysis and visualisation. It can help create business-ready charts for presentation and marketing needs. You can also use it to clean, organise, analyse and graph your data.

Statistical Analysis System (SAS)

SAS Institute developed SAS, a statistical software suite initially released in 1976. Its current version includes highly advanced features and is only available for the Windows OS. Businesses and institutions use this software to report, retrieve and analyse statistical data, forecasting, report writing and graphics. It can handle large databases easily and helps analyse extensive research data.

Open-source mining tools

Following are some of the open-source mining tools:

Konstanz Information Miner (KNIME)

KNIME is a free and open-source data analysis and integration platform written in Java and based on Eclipse. It allows users to run data analysis and build data pipelines. Users can access, merge, transform and visualise all of their data. It makes understanding data and creating data science workflows easy and accessible to everyone. Its modular environment enables easy visualisation and interactive data pipeline execution.

Orange

Orange is a powerful open-source data mining and data visualisation tool that was initially released in October 1996. It is helpful for fast prototyping and testing patterns. It also provides a platform for experiment-based selection and predictive modelling. Orange can read data sheets and format the data in patterns quickly that is interactive and you can move with widgets.

RapidMiner

RapidMiner is an open-source data science software that integrates data preparation, machine learning and data and text mining. Businesses use it for quick prototyping, research and application development. It allows users to access, load and analyse any data type, including traditional structured and unstructured data such as media, text and images.

Related: 15 Popular Data Mining Applications: A Complete Guide

Data mining tools for big data

Here are some data mining tools for big data:

Hadoop MapReduce

Hadoop MapReduce is a framework that allows writing applications to process massive amounts of data on thousands of nodes. It is an ecosystem widely used for accessing, querying and selecting big data stored in the Hadoop file system (HDFS). It is flexible, highly scalable and allows data storage and processing at an affordable cost.

Apache Spark

Apache Spark is a multi-language engine for processing data on a vast scale. It is easy to use, dynamic and allows processing complex and extensive volume data. It helps in building data applications and performing interactive data analysis. Apache Spark offers high speed as compared to other mining tools for big data and is fault-tolerant.

Qlik

Qlik provides real-time data integration and analytics cloud platform. It offers executive dashboards and business intelligence products and helps to close gaps between data, insights and actions. Users can create data visualisations, graphs, interactive dashboards and analytics apps for their usage via Qlik. It is a self-service tool, requires low maintenance and supports analytics use case at an enterprise scale.

Related: 18 Big Data Examples (Common Uses In Different Industries)

Small scale solutions for data mining

Here are some of the small scale solutions for data mining:

Scikit-learn

Scikit-learn is free software and offers simple and efficient tools for predictive data analysis. It is a robust and efficient machine learning library for the Python programming language. Scikit-learn provides statistical modelling and features various classification, regression, clustering algorithms and dimensionality reduction in the Python interface. It is easily accessible and reusable in a different context.

H2O

H2O is an open-source software used for small scale data mining. Businesses and organisations primarily use this software to analyse the data stored in their cloud infrastructure. It supports widely used statistical and machine learning algorithms, including generalised linear models, deep learning and more. It can take data directly from HDFS, Spark, Azure Data Lake or any other data source into its in-memory distributed store.

Rattle

Rattle is a popular data mining tool using the R statistical programming language. It allows partitioning databases into training, validation and testing. It summarises data statistically and visually, transforms dynamical data into modelled data, creates both unsupervised and supervised models from the data, visualises model performance and scores new datasets.

Cloud solutions for data mining

Following are some cloud solutions for data mining:

Azure machine learning

Azure machine learning is a cloud-based solution to create and manage a machine learning project lifecycle. Primarily data scientists and machine learning engineers use its existing data processing and model development skills and frameworks. It provides drag and drop components that reduce the code development and direct configuration of properties. It also helps businesses to build, test and generate advanced analytics on the basis of data.

Related: Supervised Machine Learning Examples (And How It Works)

Amazon Elastic MapReduce (EMR)

Amazon EMR is a cloud service particularly focused on analytics and runs on top of Elastic Compute Cloud (EC2) instances. It is a platform for quickly processing, analysing and applying machine learning (ML) to vast data using open-source frameworks. Businesses use Amazon EMR for securely handling big data use cases like machine learning, deep learning, bioinformatics, financial and scientific stimulation, log analysis and data transformations.

Mining tools for neural networks

Following are some mining tools for neural networks:

PyTorch

PyTorch is an open-source machine learning framework that facilitates the path from research prototyping to production deployment. It is popular for its flexibility and ease of use. The language's compatibility enables PyTorch with the widely used Python high-level programming language, which is popular among machine learning developers and data scientists.

TensorFlow

TensorFlow is a free and open-source machine learning and artificial intelligence software library. It applies to various applications but focuses particularly on the training and inference of deep neural networks. Researchers and students mostly use TensorFlow in their research projects and model building.

Mining tools for data visualisation

Here are some of the mining tools for data visualisation:

Matplotlib

Matplotlib is a feature-rich Python toolkit for creating static, animated and interactive visualisations. It provides an object-oriented API for embedding plots in applications that make use of general-purpose graphical user interface toolkits such as Tkinter or wxPython. Many users use Matplotlib interactively from the Python shell, triggering charting windows as they write commands.

Related: Top 20 Big Data Tools: Big Data And Types Of Big Data Jobs

Power BI

Microsoft provides Power BI as a business analytics service. It focuses on providing interactive visualisations and business intelligence capabilities with an interface simple enough for the end-users to make their own reports and dashboards. With the Microsoft Power BI app for Android phones, users can monitor and access their business data anywhere and anytime.

Related: Essential Power BI Interview Questions and Answers

Tableau

Tableau can help anyone see, understand and simplify their data. Business intelligence and analytics use Tableau as a visualised platform for the intentions of helping people watch, observe, understand and make decisions with a variety of data. Users can connect to almost any database, drag and drop to create visualisations and share with a click. With Tableau, you can easily make any type of graphs, plots and charts without the need for any programming.

Related: Learn About Data Science Careers (With Skills and Duties)

Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.

Explore more articles